A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning

• URL: http://arxiv.org/abs/2602.17092v1
• Date: Thu, 19 Feb 2026 05:31:03 GMT
• Title: A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning
• Authors: Aadi Joshi, Kavya Bhand,
• Abstract summary: We introduce locality radius, a measure of the minimum structural neighborhood required to determine a prediction in relational schemas.<n>We conduct a controlled empirical study across foreign key prediction, join cost estimation, blast radius regression, cascade impact classification, and additional graph-derived schema tasks.<n>Results reveal a consistent bias-radius alignment effect.
• Score: 0.3058685580689604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foreign key discovery and related schema-level prediction tasks are often modeled using graph neural networks (GNNs), implicitly assuming that relational inductive bias improves performance. However, it remains unclear when multi-hop structural reasoning is actually necessary. In this work, we introduce locality radius, a formal measure of the minimum structural neighborhood required to determine a prediction in relational schemas. We hypothesize that model performance depends critically on alignment between task locality radius and architectural aggregation depth. We conduct a controlled empirical study across foreign key prediction, join cost estimation, blast radius regression, cascade impact classification, and additional graph-derived schema tasks. Our evaluation includes multi-seed experiments, capacity-matched comparisons, statistical significance testing, scaling analysis, and synthetic radius-controlled benchmarks. Results reveal a consistent bias-radius alignment effect.

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